GLIA — A holographic memory for AI agents that isn't a graph and isn't RAG
GLIA is a new holographic memory system designed for AI agents that aims to overcome limitations of traditional methods like RAG and graphs. It stores knowledge as 1024-dimensional vectors, allowing for better associative reasoning and resilience against data loss. The system has been benchmarked against existing methods, showing significant improvements in retrieval accuracy and operational efficiency.
- ▪GLIA stores knowledge as 1024-dimensional vectors, representing patterns rather than text chunks or nodes.
- ▪It uses a technique called Circular Convolution for holographic binding, allowing relationships to coexist without traditional edges.
- ▪Benchmark tests show GLIA outperforms graph-based approaches by 2.5 times in retrieval accuracy.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3945080) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } felipe farias Posted on May 22 GLIA — A holographic memory for AI agents that isn't a graph and isn't RAG #ai #opensource #programming #machinelearning Every AI coding agent I've used (Cline, Claude, Cursor, etc) has the same problem: it forgets everything between sessions. You fix a complex race condition on Monday, and on Tuesday the agent suggests the same broken pattern again. RAG (Retrieval-Augmented Generation) is the standard fix.
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